• Journal of KIISE:Computing Practices and Letters
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• v.16 no.3
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• pp.289-300
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• 2010

Recently, diverse information which are location, call history, SMS history, photographs, and video can be collected constantly from mobile devices such as cellular phone, smart phone, and PDA. There are many researchers who study services for searching and abstraction of personal daily life with contextual information in mobile environment. In this paper, we introduce MyLifeBrowser which is developed in our previous work. Also, we explain LPS and correction of GPS coordinates as extensions of previous work and show LPS performance test and evaluate the performance of expanded keywords. MyLifeBrowser which provides searching personal information in mobile device and support of detecting related information according to a fragmentary keyword and common knowledge in ConceptNet. It supports the functionality of searching related locations using Bayesian network that is designed by the authors. In our experiment, we visualize real data through MyLifeBrowser and show the feasibility of LPS server and expanded keywords using both Bayesian network and ConceptNet.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.2
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• pp.625-630
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• 2013

Background: Hardcore smoking is represented by a subset of daily smokers with high nicotine dependence, inability to quit and unwillingness to quit. Estimating the related burden could help us in identifying a high risk population prone to tobacco induced diseases and improve cessation planning for them. This study assessed the prevalence and associated factors of hardcore smoking in three South-East Asian countries and discussed its implication for smoking cessation intervention in this region. Materials and Methods: Global Adult Tobacco Survey (GATS) data of India, Bangladesh and Thailand were analyzed to quantify the hardcore smoking prevalence in the region. On the basis of review, an operational definition of hardcore smoking was adopted that includes (1) current daily smoker, (2) no quit attempt in the past 12 months of survey or last quit attempt of less than 24 hours duration, (3) no intention to quit in next 12 months or not interested in quitting, (4) time to first smoke within 30 minutes of waking up, and (5) knowledge of smoking hazards. Logistic regression analysis was carried out using hardcore smoking status as response variable and gender, type of residence, occupation, education, wealth index and age-group as possible predictors. Results: There were 31.3 million hardcore smokers in the three Asian countries. The adult prevalence of hardcore smoking in these countries ranges between 3.1% in India to 6% in Thailand. These hardcore smokers constitute 18.3-29.7% of daily smokers. The logistic regression model indicated that age, gender, occupation and wealth index are the major predictors of hardcore smoking with varied influence across countries. Conclusions: Presence of a higher number of hardcore smoking populations in Asia is a major public health challenge for tobacco control and cancer prevention. There is need of intensive cessation interventions with due consideration of contextual predictors.

• Journal of the Korean association of regional geographers
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• v.8 no.4
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• pp.529-541
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• 2002

This paper aims to provide a context-specific interpretation on the shifting process of in-house R&D spaces in a large Korean firm in the context of rapidly changing markets and technology. Drawing on the case study of LG Electronics Company, one of the Korea's flagship companies, I examine the causes and mechanisms leading to a shift in domestic R&D spaces and the nature of learning processes between R&D teams and between R&D and other organizational units, particularly manufacturing. It appears that the current reshaping processes of domestic R&D spaces in LGE focus more on the clustering of core R&D laboratories than the geographical integration of conception and execution. However, it should not simply be viewed that such a move would be reduced to the linear model of innovation and organizational learning. Instead, it involves the firm-specific mode of regulating organizational competences. As contextual variables to induce such a firm-specific mode of organizational change, I consider the spatial form of organization, the spatial sources of knowledge and learning, and the powers of relational learning that can be made between distanciated actors and teams.

• School Mathematics
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• v.19 no.4
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• pp.691-712
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• 2017

It is an important to develop teachers' statistical reasoning or thinking by teacher education. In this study, the "comparing two data sets" tasks is focused as a way to develop pre-service elementary teachers' reasoning about core ideas of statistics such as distribution, variability, center, and spread. 6 teams of each 4 pre-service elementary teachers participated on the tasks and their presentations are analyzed based on Pfannkuch's (2006) teachers' inference model in comparing two data sets. As a result, they paid attention to the distribution and variability in the statistical problem solving by the "comparing two data sets" tasks, and used their contextual knowledge to make a statistical decision. In addition, they used some statistics and graphs as the reference for statistical communication, which is expected to provide implications for improving statistical education. The finding implies that the "comparing two data sets" tasks can be used to develop statistical reasoning of pre-service elementary teachers. Some recommendations are suggested for teacher education by these tasks.

• Journal of Digital Convergence
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• v.17 no.3
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• pp.169-180
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• 2019

The purpose of this study is to verify the effect of perceived agile culture(empowerment, continuous learning, personal communication Intensification) on job performance(task, contextual, adaptive) and to explore the moderating effect of individual capability(knowledge, skill). For the empirical analysis, data were collected from convenient sample of 219 employees working at enterprise in China. The analysis of validity and reliability of variables and regression analysis were performed using SPSS 21. The results of this research as followed: firstly, the positive perceived agile culture and job performance were statistically supported. Secondly, the individual capability played as a partial moderator on the relationship between the perceived agile culture and the job performance. The factors that constitute the perceived agile culture can present the research directions for the transformation into the agile organization.

• The Journal of the Korea Contents Association
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• v.19 no.4
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• pp.609-620
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• 2019

The purpose of this study is to examine the co-offending implementation process of robbery and burglary crime. To this end, interview data for research projects conducted by the Korea Institute of Criminology in 2013(Advancing Knowledge About Co-Offending - Burglary and Robbery in Korea) were used as secondary data. Using secondary data, we attempted a grounded theory approach. Based on the procedure presented by Strauss&Corbin (1990), the open coding stage was derived from 51 concepts, 22 subcategories and 8 upper categories. According to an analysis tool called "coding paradigm," the causal condition is the cause of the robbery and burglary crime. Contextual conditions are the formation of co-offending relationship and the reason for selecting co-offending. The central phenomenon is the co-offending implementation of robbery and burglary crime. Interventing conditions are conflict between co-offenders and occurrence of arrest factor. The action/interaction strategy is arrested all co-offenders. The consequence consisted of ending the co-offending relationship. Finally, the selective coding stage selected 'the development of conflict between formation and end of co-offending relationship' as the core category, and newly established the co-offending relationship of robbery and burglary crime through the process of 'formation-implementation-conflict-arrest-end'.

• Safety and Health at Work
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• v.10 no.1
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• pp.3-20
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• 2019

This review aimed to identify risk factors for occupational accidents and illnesses among young workers in the Nordic countries and to attain knowledge on specific vulnerable groups within the young working force that may need special attention. We conducted a systematic review from 1994 to 2014 using five online databases. Of the 12,528 retrieved articles, 54 met the review criteria and were quality assessed, in which data were extracted focusing on identifying occupational safety, health risk factors, and vulnerable groups among the young workers. The review shows that mechanical factors such as heavy lifting, psychosocial factors such as low control over work pace, and organizational factors such as safety climate are all associated with increased injury risk for young Nordic workers. Results show that exposures to chemical substances were associated with skin reactions, e.g., hand eczema. Heavy lifting and awkward postures were risk factors for low back pain, and high job demands were risk factors for mental health outcomes. The review identified young unskilled workers including school drop-out workers as particularly vulnerable groups when it comes to occupational accidents. In addition, apprentices and young skilled workers were found to be vulnerable to work-related illnesses. It is essential to avoid stereotyping young Nordic workers into one group using only age as a factor, as young workers are a heterogeneous group and their vulnerabilities to occupational safety and health risks are contextual. Politicians, researchers, and practitioners should account for this complexity in the education, training and organization of work, and workplace health and safety culture.

• The Korean Journal of Archival Studies
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• no.67
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• pp.99-138
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• 2021

In the era of big data, analyzing not only structured data but also unstructured data is emerging as an important task. Official documents produced by government agencies are also subject to big data analysis as large text-based unstructured data. From the perspective of internal work efficiency, knowledge management, records management, etc, it is necessary to analyze big data of public documents to derive useful implications. However, since many of the public documents currently held by public institutions are not in open format, a pre-processing process of extracting text from a bitstream is required for big data analysis. In addition, since contextual metadata is not sufficiently stored in the document file, separate efforts to secure metadata are required for high-quality analysis. In conclusion, the current official documents have a low level of machine readability, so big data analysis becomes expensive.

• Education of Primary School Mathematics
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• v.25 no.4
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• pp.297-312
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• 2022

Word problems can lead learners to more meaningfully learn mathematics by providing learners with various problem-solving experiences and guiding them to apply mathematical knowledge to the context. This study attempted to provide implications for the textbook writing and teaching and learning process by examining the word problem of elementary mathematics textbooks from the perspective of practical context. The word problem of elementary mathematics textbooks was examined, and elementary mathematics textbooks in the United States and Finland were referenced to find specific alternatives. As a result, when setting an unnatural context or subject to the word problem in elementary mathematics textbooks, artificial numbers were inserted or verbal expressions and illustrations were presented unclearly. In this case, it may be difficult for learners to recognize the context of the word problem as separate from real life or to solve the problem by understanding the content required by the word problem. In the future, it is necessary to organize various types of word problems in practical contexts, such as setting up situations in consideration of learners in textbooks, actively using illustrations and diagrams, and organizing verbal expressions and illustrations more clearly.

• Smart Structures and Systems
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• v.31 no.4
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• pp.325-334
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• 2023

Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.